The sea-surface microlayer (SML) is the boundary layer at the air-sea interface where many biogeochemical processes occur. Many organisms (e.g., bacteria) produce surface active agents (surfactants) for life processes, which accumulate in the SML and dampen short gravity-capillary waves, resulting in sea surface slicks. Synthetic aperture radar (SAR) is capable of remotely sensing these features on the sea surface by measuring reflected backscatter from the ocean surface in microwaves. This study coordinates SAR overpasses with in situ SML and subsurface (SSW) microbial sample collection to guide subsequent analysis after 16s rRNA sequencing on the Illumina MiSeq. In April 2017, 138 SML and SSW samples were collected near a targeted oil-seep where the Taylor Platform was knocked down in the Gulf of Mexico, both in and out of visually-observed oil slicks. In July and August 2018, 220 SML and SSW samples were collected near the Looe Key coral reef and a coastal seagrass area. Analysis of microbial abundance and diversity between the two experiments shows that within oil slicks, surfactant- and oil-associated bacteria prefer to reside within the SSW rather than in the SML. In natural slicks in the coastal seagrass area, these bacteria are more abundant in the SML. Outside of these slicks, surfactant-associated bacteria are more abundant within the SML than the SSW. This suggests that the presence of oil reduces the habitability of the SML, whereas natural slicks created by foam and other surfactants creates a more habitable environment in the SML. With lower wind speed, abundance of these bacteria are greater, as increased wind speed results in a harsher environment. The diurnal cycle had an effect on the relative abundance of surfactant-associated bacteria in the SML and SSW. Our results demonstrate the usefulness of synthetic aperture radar to remotely sense sea surface slicks in coordination with in situ surfactant-associated bacteria data collection of the sea surface slicks.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21089号 / 工博第4453号 / 新制||工||1692(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 立川 康人, 准教授 後藤 忠徳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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The Phase Gradient Autofocus (PGA) algorithm is widely used in spotlight mode SAR for motion compensation. The Maximum Likelihood PGA (ML PGA) algorithm has been shown to be a superior autofocus method. The PGA is restricted to high altitude aircraft. Since lower altitude SARs have significant range dependencies that cannot be ignored, the PGA could not be used. This thesis eliminates the high altitude restriction and extends the PGA for use with all spotlight SARs. The new algorithm is tested with three images. Each image has a unique quality. A desert image provides a low signal to clutter ratio with no distinct targets and the mountain image has areas with high signal-to-clutter and areas with low signal-to-clutter. Each image was corrupted with a low frequency and high frequency motion induced low altitude phase error. The new Phase Weighted Estimation (PWE) low altitude autofocus method converged to a lower standard deviation than the ML PGA, but required more iterations. Another limitation of the PGA is that it will only work for spotlight SAR. In this thesis, the spotlight PGA is extended to stripmap by using a conversion similar to spotlight mode. With the space frequency relationship an altered PGA is used to extend the PGA to stripmap mode SAR. The stripmap SAR, range dependant PGA allows for focusing of low altitude low cost stripmap SARs. The phase weighted estimation method is extended to range dependent stripmap. The stripmap mode estimator is most successful with high signal-to-noise images.

Interferometry and coherent change detection (CCD) utilize phase differences between complex SAR images to find terrain height and to detect small changes between images, respectively. A new method for improving interferometry and CCD using multiple sub-apertures is proposed. Using backprojection processing, multiple sub-aperture images are created for a pair of flights. An interferogram and coherence map is made from each sub-aperture. For CCD, each sub-aperture coherence map offers an independent estimate of the coherence over the same area. By combining coherence maps, low coherence areas associated with residual motion errors are reduced, shadowed areas are minimized, and the overall coherence of stationary objects between images is increased. For interferometry, combining independent estimates of a scene's height offers a more accurate height estimate. For repeat-pass interferometry, multiple apertures are shown to increase the coverage of valid height estimates. The benefits of multi-aperture interferometry and CCD are shown using examples with real data.

[pt] A captura de cenas de cobertura da Terra com sensores óticos de satélite é frequentemente limitada pela presença de nuvens que corrompem as imagens coletadas. Entre os métodos para recuperar imagens óticas de satélite corrompidas por nuvens, várias abordagens de tradução de imagemimagem usando Redes Adversárias Generativas (GANs) têm surgido com bons resultados, conseguindo criar imagens óticas realistas a partir de imagens de Radar de Abertura Sintética (SAR). Os métodos baseados em GANs condicionais (cGAN) propostos até agora para a síntese de imagens SAR-óticas tendem a produzir imagens ruidosas e com pouca nitidez. Neste trabalho, propomos a atrous-cGAN, uma nova arquitetura que melhora a transformação de imagem SAR em ótica. As redes propostas para o gerador e discriminador contam com convolusões dilatadas (atrous) e incorporam o módulo Pirâmide Espacial Atrous Pooling (ASPP) para realçar detalhes finos na imagem ótica gerada, explorando o contexto espacial em várias escalas. Este trabalho apresenta experimentos realizados para avaliar o desempenho da atrous-cGAN na síntese de imagens Landsat a partir de dados Sentinel-1A, usando quatro bases de dados públicas. A análise experimental indicou que a atrous-cGAN supera o modelo clássico pix2pix como uma ferramenta de aprendizado de atributos para segmentação semântica. A proposta também gera imagens com maior qualidade visual, e em geral com maior semelhança com a verdadeira imagem ótica. / [en] The capture of land cover scenes with optical satellite sensors is often constrained by the presence of clouds that corrupt the collected images. Among the methods for recovering satellite optical images corrupted by clouds, several image to image translation approaches using Generative Adversarial Networks (GANs) have emerged with profitable results, managing to create realistic optical images from Synthetic Aperture Radar (SAR) data. Conditional GAN (cGAN) based methods proposed so far for SAR-to-optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work, we propose the atrous-cGAN, a novel cGAN architecture that improves the SAR-to-optical image translation. The proposed generator and discriminator networks rely on atrous convolutions and incorporate the Atrous Spatial Pyramid Pooling (ASPP) module to enhance fine details in the generated optical image by exploiting spatial context at multiple scales. This work reports experiments carried out to assess the performance of atrouscGAN for the synthesis of Landsat images from Sentinel-1A data based on four public datasets. The experimental analysis indicated that the atrouscGAN overcomes the classical pix2pix model as a feature learning tool for semantic segmentation. The proposal also generates higher visual quality images, in general with higher similarity with the true optical image.

Microwave imaging finds numerous applications involving optically obscured targets. One particular area is breast cancer detection, since microwave technology promises fast low-cost image reconstruction without the use of harmful radiation typical of X-ray mammography. However, the success of microwave imaging is hindered by a critical issue, the complex nature of near-field electromagnetic scattering in tissue. To overcome this, specialized image reconstruction algorithms alongside sensitive measurement hardware are required. In this work, real-time near-field microwave imaging algorithms known as quantitative microwave holography and scattered power mapping are explored. They are experimentally demonstrated to identify potential tumor regions in tissue phantoms. Alongside this development, quality control techniques for evaluating microwave hardware are also described. Two new methods for improving the image reconstruction quality are also presented. First, a novel technique, which combines two commonly used mathematical approximations of scattering (the Born and Rytov approximations), is demonstrated yielding improved image reconstructions due to the complimentary nature of the approximations. Second, a range migration algorithm is introduced which enables near-field refocusing of a point-spread function (PSF), which is critical for algorithms that rely on measured PSFs to perform image reconstruction. / Thesis / Doctor of Philosophy (PhD) / Breast cancer remains as one of the highest causes of cancer-related deaths in women in Canada. Though X-ray mammography remains the gold standard for regular breast cancer screening, its use of harmful radiation, painful breast compression, and radiologist dependent evaluation remain as detracting factors for its use. Over the past 40 years, researchers have been exploring the use of microwave technology in place of X-ray mammography. Microwave radiation, used at power levels similar to that of a cellphone, has been demonstrated successfully in simulations of breast scans. However, in experimental evaluations with breast phantoms, the complex scattering path of the radiation through tissue complicates image reconstruction. In this thesis, methods of improving the accuracy of microwave algorithms are explored, alongside new breast phantom structures that replicate well the electrical properties of tissue. The results of this work demonstrate the flexibility of microwave imaging, and the adversities that still need to be overcome for it to begin seeing clinical use.

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Floods are the most frequent disaster in the world and flood exposure is increasing globally. Flood mapping of past events can be a useful aid in not only disaster risk management, but also in evaluating and validating global flood models (GFMs) which are being used to assess and predict these floods. There are however numerous ways of mapping floods, and it is uncertain how well these perform as validation data. In this paper, a change detection approach based on a combination of synthetic aperture radar (SAR) and cloud-computing to map past flood events is evaluated (hereinafter CD- SAR). Eight flood events were chosen over a wide range of hydroclimatic conditions, regions, and flood types. These eight events were mapped with CD-SAR and compared to four GFM outputs and two flood maps of past events from two commonly used databases. The spatial agreement between CD-SAR and the GFMs showed a considerable variation between regions and models. The agreement was however shown to share a similar interval to previous validation studies, albeit in the lower portion. CD-SAR also showed similar performance to the comparison between the GFMs and the outputs from the databases of mapped past flood events. The results were also analysed for how the flood extent and flood-edge distribution of CD-SAR compare to both the GFMs and the database outputs. The results showed a similar variation in distribution as the spatial agreement but did not follow the same trend for all regions. The flexibility and high resolution of CD-SAR allow it to cover events over a wider range of regions and of any size and it can be a viable tool to map past flood events and could be used to evaluate GFMs. However, CD-SAR needs further evaluation as uncertainties still exist due to the inherent characteristics of SAR and the revisit times of the satellites using SAR. / Översvämningsrelaterade katastrofer är de mest förekommande i världen och exponeringen för översvämningar ökar globalt. Kartläggningar av tidigare översvämningshändelser kan vara ett användbart verktyg för katastrofriskhantering samt för att utvärdera och validera globala översvämningsmodeller som används för att uppskatta och förutse översvämningar. Det finns ett flertal sätt att kartlägga översvämningar men det är osäkert hur bra de fungerar som valideringsdata. Denna studie utvärderar en metod för att kartlägga översvämningar genom förändringsdetektering i syntetiska aperturradarbilder (SAR) med hjälp av molntjänster (härefter CD-SAR). Åtta översvämningshändelser valdes över en rad hydroklimatiska förhållanden, regioner och översvämningstyper. De här översvämningshändelserna kartlades med hjälp av CD-SAR och jämfördes med fyra globala översvämningsmodeller och två översvämningskartor från två väl använda databaser. CD-SAR och de fyra globala översvämningsmodellerna visade betydliga skillnader i överenstämmelse mellan både regioner och modeller. Överenstämmelsen var dock inom samma intervall som påvisats i tidigare studier om än något lägre. CD-SAR visade sig också ha en liknande överensstämmelse som jämförelsen mellan modellerna och databaserna. En analys av CD- SAR jämfört med modellerna och databaserna genomfördes över utbredningen av översvämningarna och dess fördelningar. Resultaten visade en liknande variation som överenstämmelsen men följde inte samma trender för alla regioner. CD-SAR:s flexibilitet och dess höga upplösning gör att den kan omfatta ett stort antal översvämningshändelser över en rad olika regioner. Det gör det möjligt för CD- SAR att genomföra översvämningskartläggningar och för att utvärdera globala översvämningsmodeller. CD-SAR behöver dock utvärderas ytterligare då metoden fortfarande innehar vissa osäkerheter. Osäkerheterna är till mestadels på grund av de inneboende egenskaperna hos SAR samt täckningen för satelliterna som använder SAR.